Land-use classification using remote sensing images covers a wide range of applications. With more detailed spatial and textural information provided in very high resolution (VHR) remote sensing ...images, a greater range of objects and spatial patterns can be observed than ever before. This offers us a new opportunity for advancing the performance of land-use classification. In this paper, we first introduce an effective midlevel visual elementsoriented land-use classification method based on "partlets," which are a library of pretrained part detectors used for midlevel visual elements discovery. Taking advantage of midlevel visual elements rather than low-level image features, a partlets-based method represents images by computing their responses to a large number of part detectors. As the number of part detectors grows, a main obstacle to the broader application of this method is its computational cost. To address this problem, we next propose a novel framework to train coarse-to-fine shared intermediate representations, which are termed "sparselets," from a large number of pretrained part detectors. This is achieved by building a single-hidden-layer autoencoder and a single-hidden-layer neural network with an L0-norm sparsity constraint, respectively. Comprehensive evaluations on a publicly available 21-class VHR landuse data set and comparisons with state-of-the-art approaches demonstrate the effectiveness and superiority of this paper.
3-D shape analysis has attracted extensive research efforts in recent years, where the major challenge lies in designing an effective high-level 3-D shape feature. In this paper, we propose a ...multi-level 3-D shape feature extraction framework by using deep learning. The low-level 3-D shape descriptors are first encoded into geometric bag-of-words, from which middle-level patterns are discovered to explore geometric relationships among words. After that, high-level shape features are learned via deep belief networks, which are more discriminative for the tasks of shape classification and retrieval. Experiments on 3-D shape recognition and retrieval demonstrate the superior performance of the proposed method in comparison to the state-of-the-art methods.
As an important part of prognostics and health management, accurate remaining useful life (RUL) prediction for lithium (Li)-ion batteries can provide helpful reference for when to maintain the ...batteries in advance. This paper presents a novel method to predict the RUL of Li-ion batteries. This method is based on the framework of improved particle learning (PL). The PL framework can prevent particle degeneracy by resampling state particles first with considering the current measurement information and then propagating them. Meanwhile, PL is improved by adjusting the number of particles at each iteration adaptively to reduce the running time of the algorithm, which makes it suitable for online application. Furthermore, the kernel smoothing algorithm is fused into PL to keep the variance of parameter particles invariant during recursive propagation with the battery prediction model. This entire method is referred to as PLKS in this paper. The model can then be updated by the proposed method when new measurements are obtained. Future capacities are iteratively predicted with the updated prediction model until the predefined threshold value is triggered. The RUL is calculated according to these predicted capacities and the predefined threshold value. A series of case studies that demonstrate the proposed method is presented in the experiment.
Analog circuits have been extensively used in industrial systems, and their failure may make the systems work abnormally and even cause accidents. In order to monitor their status, detect faults, and ...predict their failure early, this study proposes a signal model-based fault coding to monitor the circuit response after being stimulated to perform a fault diagnosis without training a large amount of sample data and fault classifiers. Manifold features extracted from circuit responses are associated with a fault-indicating curve in the feature space, in which a group of fault bases are uniformly and continuously distributed along with gradual deviation from the nominal value of one critical component. These bases can be deployed in a factory setting but used during field operation. Fault coding is converted to a novel optimization problem, and the optimized solution forms a fault code representing fault class, suitable for realizing fault detection, and isolation for different components. A fault indicator based on comparison between fault codes can describe performance degradation trends. To improve the prediction accuracy, historical degradation data are collected and considered as a priori exemplars, and a novel exemplar-based conditional particle filter is proposed to track a degradation process for the prediction of remaining useful performance. Case studies on two analog filter circuits demonstrate that the proposed method achieves relatively high fault diagnosis and prognosis accuracy. The main advantages of our study are two-fold: first, the high diagnostic accuracy can still be obtained even if there is no large amount of training data; second, the prognostic effect remains relatively stable whenever triggering prognosis module.
Automatic detection of geospatial targets in cluttered scenes is a profound challenge in the field of aerial and satellite image analysis. In this paper, we propose a novel practical framework ...enabling efficient and simultaneous detection of multi-class geospatial targets in remote sensing images (RSI) by the integration of visual saliency modeling and the discriminative learning of sparse coding. At first, a computational saliency prediction model is built via learning a direct mapping from a variety of visual features to a ground truth set of salient objects in geospatial images manually annotated by experts. The output of this model can predict a small set of target candidate areas. Afterwards, in contrast with typical models that are trained independently for each class of targets, we train a multi-class object detector that can simultaneously localize multiple targets from multiple classes by using discriminative sparse coding. The Fisher discrimination criterion is incorporated into the learning of a dictionary, which leads to a set of discriminative sparse coding coefficients having small within-class scatter and big between-class scatter. Multi-class classification can be therefore achieved by the reconstruction error and discriminative coding coefficients. Finally, the trained multi-class object detector is applied to those target candidate areas instead of the entire image in order to classify them into various categories of target, which can significantly reduce the cost of traditional exhaustive search. Comprehensive evaluations on a satellite RSI database and comparisons with a number of state-of-the-art approaches demonstrate the effectiveness and efficiency of the proposed work.
Fault detection and isolation (FDI) is very difficult for electronics-rich analog systems due to its sophisticated mechanism and variable operational conditions. Traditionally, FDI in such systems is ...done through the monitoring of deviation of output signals in voltage or current at system level, which commonly arises from the degradation of one or more critical components. Therefore, FDI can be transformed to a multiclass classification task given the extracted features of the output signals in voltage or current of the circuit. Traditional feature extraction on the circuit output is mostly based on time-domain, frequency-domain, or time-frequency signal processing, which collapse high-dimensional raw signals into a lower dimensional feature set. Such low-dimensional feature set usually suffers from information loss so as to affect the accuracy of the later fault diagnosis. In order to retain as much information as possible, deep learning is proposed which employs a hierarchical structure to capture the different levels of semantic representations of the signals. In this paper, a novel fault diagnostic application of Gaussian-Bernoulli deep belief network (GB-DBN) for electronics-rich analog systems is developed which can more effectively capture the high-order semantic features within the raw output signals. The novel fault diagnosis is validated experimentally on two typical analog filter circuits. Experimental results show the fault diagnosis based on GB-DBN is with superior diagnostic performance than the traditional feature extraction methods.
Human Motion Tracking by Multiple RGBD Cameras Liu, Zhenbao; Huang, Jinxin; Han, Junwei ...
IEEE transactions on circuits and systems for video technology,
09/2017, Letnik:
27, Številka:
9
Journal Article
Recenzirano
The advent of low-cost depth cameras, such as the Microsoft Kinect in the consumer market, has made many indoor applications and games based on motion tracking available to the everyday user. ...However, it is a large challenge to track human motion via such a camera because of its low-quality images, missing depth values, and noise. In this paper, we propose a novel human motion capture method based on a cooperative structure of multiple low-cost RGBD cameras, which can effectively avoid these problems. This structure can also manage the problem of body occlusions that appears when a single camera is used. Moreover, the whole process does not require training data, which makes this approach easily deployed and reduces operation time. We use the color image, depth image, and point cloud acquired in each view as the data source, and an initial pose is extracted in our optimization framework by aligning multiple point clouds from different cameras. The pose is dynamically updated by combining a filtering approach with a Markov model to estimate new poses in video streams. To verify the efficiency and robustness of our approach, we capture a wide variety of human actions via three cameras in indoor scenes and compare the tracking results of the proposed method to those of the current state-of-the-art methods. Moreover, our system is tested on more complex situations, in which multiple humans move within a scene, possibly occluding each other to some extent. The actions of multiple humans are tracked simultaneously, which would assist group behavior analysis.
Extracting local features from 3D shapes is an important and challenging task that usually requires carefully designed 3D shape descriptors. However, these descriptors are hand-crafted and require ...intensive human intervention with prior knowledge. To tackle this issue, we propose a novel deep learning model, namely circle convolutional restricted Boltzmann machine (CCRBM), for unsupervised 3D local feature learning. CCRBM is specially designed to learn from raw 3D representations. It effectively overcomes obstacles such as irregular vertex topology, orientation ambiguity on the 3D surface, and rigid or slightly non-rigid transformation invariance in the hierarchical learning of 3D data that cannot be resolved by the existing deep learning models. Specifically, by introducing the novel circle convolution, CCRBM holds a novel ring-like multi-layer structure to learn 3D local features in a structure preserving manner. Circle convolution convolves across 3D local regions via rotating a novel circular sector convolution window in a consistent circular direction. In the process of circle convolution, extra points are sampled in each 3D local region and projected onto the tangent plane of the center of the region. In this way, the projection distances in each sector window are employed to constitute a novel local raw 3D representation called projection distance distribution (PDD). In addition, to eliminate the initial location ambiguity of a sector window, the Fourier transform modulus is used to transform the PDD into the Fourier domain, which is then conveyed to CCRBM. Experiments using the learned local features are conducted on three aspects: global shape retrieval, partial shape retrieval, and shape correspondence. The experimental results show that the learned local features outperform other state-of-the-art 3D shape descriptors.
The discriminability of the bag-of-words representations can be increased via encoding the spatial relationship among virtual words on 3D shapes. However, this encoding task involves several issues, ...including arbitrary mesh resolutions, irregular vertex topology, orientation ambiguity on 3D surface, invariance to rigid, and non-rigid shape transformations. To address these issues, a novel unsupervised spatial learning framework based on deep neural network, deep spatiality (DS), is proposed. Specifically, DS employs two novel components: spatial context extractor and deep context learner. Spatial context extractor extracts the spatial relationship among virtual words in a local region into a raw spatial representation. Along a consistent circular direction, a directed circular graph is constructed to encode relative positions between pairwise virtual words in each face ring into a relative spatial matrix. By decomposing each relative spatial matrix using singular value decomposition, the raw spatial representation is formed, from which deep context learner conducts unsupervised learning of the global and local features. Deep context learner is a deep neural network with a novel model structure to adapt the proposed coupled softmax layer, which encodes not only the discriminative information among local regions but also the one among global shapes. Experimental results show that DS outperforms state-of-the-art methods.
In this letter, we develop a novel framework of leveraging weakly supervised learning techniques to efficiently detect targets from remote sensing images, which enables us to reduce the tedious ...manual annotation for collecting training data while maintaining the detection accuracy to large extent. The proposed framework consists of a weakly supervised training procedure to yield the detectors and an effective scheme to detect targets from testing images. Comprehensive evaluations on three benchmarks which have different spatial resolutions and contain different types of targets as well as the comparisons with traditional supervised learning schemes demonstrate the efficiency and effectiveness of the proposed framework.
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